Download Infographic: Must Read Books in Data Science / Analytics

Hey there !

You can think of this infographic as an ideal list of books to have in bookshelf of every data scientist / analyst. These books cover a wide range of topics and perspective (not only technical knowledge), which should help you become a well rounded data scientist.

If you have other suggestions, please feel free to add them below:

Books related to data science decisioning:

When Genius Failed: The Rise and fall of Long-Term Capital Management A fast paced thriller, this book not only brings out how you can compete on data based decisions, but also why you need to keep human behavior in mind while taking decisions on data.

Scoring Points: How Tesco Continues to Win Customer Loyalty this book brings out some of the practical challenges faced by Tesco and how they overcame them to create one of the biggest success story of customer loyalty.

The Signal and the Noise: The Art and Science of Prediction . From the stock market to the poker table, from earthquakes to the economy, Nate Silver takes us on an enthralling insider’s tour of the high-stakes world of forecasting, showing how we can use information in a smarter way amid a noise of data – and make better predictions in our own lives. The book tells not only about the errors made by analysts, but also how to avoid them.

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die In this rich, entertaining primer, Siegel reveals the power and perils of prediction by including case studies from across the globe. Aimed towards a common man, the book explains predictive modeling and its basics in lay man terms.

Moneyball: Inspiring story of a baseball team manager with low budget to run the team, who uses statistics to identify undervalued players and carves out a winning team out of them.

Freakonomics: Probably the first book where I read on how data and analysis can be used to revel unknown insights. While analytics has evolved significantly since the time this book had come out, this book is still worth a read for the analysis it presents and delivers from the tools of previous era.

Books related to hands on data science:

**R Cookbook by Paul Teetor:** This is simply the best book to start your journey with R. It contains tons of examples and practical advice on a wide range of topics like file input / output, data manipulations, merging and sorting to building a regression model

Machine Learning for Hackers by Drew Conway & John Myles White This book is meant for data scientists and not hackers. I don’t know why the title says so. A very practical manual for learning machine learning, it comes with good visuals and code on R.

R graphics cookbook by Winston Chang There is no better way for visualization, but to learn ggplot2. Sadly, learning ggplot2 might seem like learning a completely new language in itself. This is where this “cookbook” comes to rescue. The recipes from Winston are short, sweet and to the point

Programming Collective Intelligence by Toby Segaran (popularly referred as PCI) The book was written long before data science and machine learning acquired the cult status they have today – but the topics and chapters are entirely relevant even today! Some of the topics covered in the book are collaborative filtering techniques, search engine features, Bayesian filtering and Support vector machines.

Python for Data Analysis by Wes McKinney Except for the title of the book (which I find misleading), I like everything else about this book. It contains ample codes and examples to leave you capable of performing any operation / transformation on a dataframe in Python (using pandas).

Books on Web Analytics:

Web Analytics: An Hour a Day by Avinash Kaushik Discover how to move beyond clickstream analysis, why qualitative data should be your focus, and more insights and techniques that will help you develop a customer-centric mind-set without sacrificing your company’s bottom line.

Web Analytics 2.0 by Avinash Kaushik It provides specific recommendations for creating an actionable strategy, applying analytical techniques correctly, solving challenges such as measuring social media and multi-channel campaigns, achieving optimal success by leveraging experimentation, and employing tactics for truly listening to your customers

How to Measure Social Media: A Step-By-Step Guide to Developing and Assessing Social Media ROI by Nichole Kelly This book will give you simple step-by-step techniques for creating measurable strategies and getting the data to prove they deliver. You’ll also get helpful hands-on exercise worksheets.

Data Visualization:

The Visual Display of Quantitative Information by Edward R. Tufte: First published in 1983, a classic book on charts, tables and various practices in design of data graphics. The book contains 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis

Visualize This: The FlowingData Guide to Design, Visualization, and Statistics by Nathan Yau: provides approaches to tell stories with data and offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design. It also provides details on tools that can be used to visualize data-native graphics for the Web and tools to design graphics for print.

The Wall Street Journal Guide to Information Graphics: The Dos & Don’ts of Presenting Data, Facts, & Figures by Dona M. Wong: A super practical guide to effective communication through graphs and charts. Concise, well written and easy to navigate, this book is a must read for people who have just started making presentations

Show me the Numbers: Designing Tables and Graphs to Enlighten by Stephen Few: It gives you the tools to create effective tables and charts, and the understanding on how and why these tools work. The book has a lot of practical advice which can be applied with Excel and hence can be put in practice straight away.

Now You See It: Simple Visualization Techniques for Quantitative Analysis by Stephen Few: Few talks about principles of visualization and their applications. Again, most of the learnings can be applied on Excel.

Information Dashboard Design: The Effective Visual Communication of Data by Stephen Few: This book will teach you the visual design skills you need to create dashboards that communicate clearly, rapidly, and compellingly.

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Must Read Books in Data Science.pdf (1.1 MB)

I will include the following books by Nassim Nicholas Taleb:

  • Fooled by Randomness
  • The Black Swan: The impact of the highly improbable

Both of them are good reads for analytics professionals.

If Someone is very new to stats I would recommend to start with - Statistics in Plain English

Once you are done with this you can move to next level - An Introduction to Statistical Learning: with Applications in R

Both these texts have free PDFs available on net !!

1 Like

Probably it refers to the original meaning of the term ‘hacker’.

BTW, just yesterday I noticed that Pedro Domingos, a CS Prof. from the University of Washington, who also has an ML course on Coursera, has written a book on ML for lay audiences.

One more formal oriented book next step after “An introduction to statistical learning” and a great reference when you want to check again one algorithm because you forgot or overlook one point.
Machine Leaning - The art and science of algorithms that make sense of data

R for Data Science
by: Garrett Grolemund and Hadley Wickham

This is an extremely well written and practical reference book. Moreover, I believe, for beginners to R this is a good book to start.

You can read it online at R for Data Science

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The link to the PDF version is broken. Please fix it.

Thank you

I agree! ‘R for Data Science’ is a wonderful book, to begin with. I would recommend it over ‘R Cookbook’.

Great recommendations! I will definitely check some of these out. While it’s not a book, I learned a lot from this video course on data visualization

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